Managing Master Data Implementations
https://doi.org/10.15514/ISPRAS-2025-37(4)-9
Abstract
Every business organization has a subset of data which must be highly consistent: legal information, supplier and contractual data, customer base, etc. Customers and employees expect to receive the same information about the same data object from different organization sources, which are usually other information systems. The process of consolidation and centralized control of such data throughout the organization is called Master Data Management (MDM). The iterative deployment strategy is a popular way to introduce MDM to a organization that supposes a step-by-step implementation of MDM components based on the real needs of the organization. In this paper, we present a functional MDM model for the early stages of MDM implementation within the iterative deployment strategy. The purpose of this model is to represent real business needs of an organization in terms of MDM, making clear which MDM components should be implemented, and which should not. Detailed description of the model components is provided. Also, a case study, presenting a portfolio of six real MDM projects analyzed from the viewpoint of the proposed model is performed.
About the Authors
Sergey Viktorovich KUZNETSOV
Saint Petersburg State University
Russian Federation
Russian Federation
Lecture of St.Petersburg University (Applied Cybernetics Chair), CEO of Unidata Ltd since 2014. Research interests: data government, master data management, graph algorithms, deep learning.
Dmitry Vladimirovich KOZNOV
Saint Petersburg State University
Russian Federation
Russian Federation
Dr. Sci. (Tech.), Professor of the System Programming Department of St. Petersburg State University. Research interests: software engineering, model-driven software development & dsl (in particular, for enterprise applications and telecom), machine learning and neural networks for software data, technical documentation.
Dmitry Vadimovich LUCIV
Saint Petersburg State University
Russian Federation
Russian Federation
PhD in computer science, associate professor оf System Programming Department at Saint Petersburg State University, Russia. Research interests: software engineering, software data analysis, documentation analysis, systems programming.
References
1. Khatri V., Brown C.V. Designing data governance. Communications of the ACM, 53(1), 2010, pp. 148– 152.
2. DAMA-DMBOK: Data Management Body of Knowledge, 2017. 588 p.
3. Andrichenko A.N. Tendencies and condition in the field of reference data management in the engineering industry. Ontology of designing, 2 (4), 2012, pp. 25–35. (in Russian).
4. Nemtsov E.F. Reference data in the intelligent railway transport management system. Automation, communication and Informatic, 2, 2020, pp. 15–18. (in Russian).
5. Golubev S.S., Lotsmanov A.N., Kuzin A. Y., Soloviev V.G., Kozlov A.D., Grigoriev B.A. The branch system of the National Standard Reference Data Service for the Oil and Gas Complex. Legislative and applied metrology, 3, 2020, pp. 12–16. (in Russian).
6. Yanchenko G.А. On the standard reference data density properties of rocks. Mining information and analytical bulletin, 8, 2011, pp. 111–115. (in Russian).
7. Chigirinsky Y., L. Method of reliability improvement of reference data. Izvestia Volgogradskogo universitets, 13 (86), 2011, pp. 55–61. (in Russian).
8. Gartner Glossary, https://www.gartner.com/en/glossary, accessed 30.02.2025.
9. Walker S. Dayley A., Parker S., Hawker M. Magic Quadrant for Master Data Management. Gartner, 2021.
10. Zmud R.W. An Examination of ‘Push-Pull’ Theory Applied to Process Innovation in Knowledge Work. Management Science, 30 (6), 1984, pp. 727–738.
11. Ladley J. Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program. Academic Press; 2nd edition, 2019. 350 p.
12. Kuznetsov S.V., Koznov D.V. Master data management in an iterative approach. Ontology of designing, 11(2), 2021, pp. 170–184. (in Russian).
13. Silvola R., Jääskelainen O., Kropsu-Vehkaperä H., Haapasalo H. Managing one master data – Challenges and preconditions. Industrial Management & Data Systems, 111(1), 2011, pp. 146–162.
14. Yourdon E., Constantine L.L. Structured Design: Fundamentals of a Discipline of Program and Systems Design. Yourdon Press, 1975. 348 p.
15. Jacobson I. Object-Oriented Software Engineering. ASM Press, 1992. 528 p.
16. Ould M.A. Business Processes: Modelling and Analysis for Re-Engineering and Improvement. Wiley, 1995.
17. Vassiliadis P., Simitsis A., Skiadopoulos S. Conceptual modeling for ETL processes. DOLAP, 2002, pp. 14–21.
18. CMMI Data Management Maturity Model (DMM). CMMI Institute (website). http://bit.ly/1Vev9xx, accessed 30.01.2025.
19. IBM Data Governance Council Maturity Model https://ibm.co/2sRfBIn, accessed 30.01.2025.
20. Velocity Methodology. Best Practices. Informatica. 2008.
21. O'Kane B., Moran M. P. The Seven Building Blocks of MDM: A Framework for Success. August 2016. Gartner. ID: G00311161.
22. Ofner M.H., Straub K., Otto B., Österle H. Management of the master data lifecycle: a framework for analysis. J. Enterp. Inf. Manag, 26(4), 2013, pp. 472–491.
23. Gavrilova T.A., Kudryvtsiv D. V. Knowledge management: from words to business. Intelligent Enterprise: RE, 12–13 (101), 2004, pp. 48. (in Russian).
24. Gavrilova T.A. Knowledge presentation in expert system АВТАНТЕСТ. Izvestia Acadimii Nauk USSR, 5, 1984, pp. 165–173. (in Russian).
25. Koznov D.V., Peregudov A.F., et al. Visual environment for designing broadcast software. Sistemnoye programmirovaniye, 2 (1), 2006, pp. 142–168. (in Russian).
26. Ivanov A., Koznov D., et al. Behavior model RTST++. Zapiski seminara kafedry sistemnogo programmirovaniya "Case-tool RTST++", 1, 1998, pp. 37–52. (in Russian).
Review
For citations:
KUZNETSOV S.V., KOZNOV D.V., LUCIV D.V. Managing Master Data Implementations. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2025;37(4):161-176. https://doi.org/10.15514/ISPRAS-2025-37(4)-9
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